Data Warehouses are commonly designed using Dimensional Modeling. Such a Data Warehouse is known as a Dimensional Data Warehouse. Typically data in a Dimensional Data Warehouse is stored in fact tables that connect to dimension tables in a design known as star-schema. Sourced from the base data in star-schemas, multidimensional structures called Online Analytical Processing (OLAP) cubes are often built for analysis and reporting purposes.
The base data in a Dimensional Data Warehouse is stored in a Relational database. Relational databases use a collection of relations e.g. tables, to define a relational model to which the relational database conforms. In relational databases, the data is typically accessed through the use of a Structured Query Language (SQL) type query.
The OLAP cubes may be implemented in a relational database. This is known as Relational OLAP (ROLAP), or in a multidimensional database environment, as Multidimensional OLAP (MOLAP).
In OLAP, the structure of the database allows rapid processing of the data such that queries can be answered quickly with reduced processor burden. This is facilitated by the use of a data cube which represents the dimensions of data available. For example, “Sales” could be viewed in the dimensions of Item, Product, Geography, Time, or some additional dimension. In this case, “Sales” is referred to as the measure attribute of the data cube and the other dimensions are referred to as the feature attribute. A database creator can also define hierarchies and levels within a dimension, such as cosmetics-skin care products-lotion, with an associated hierarchy within the dimension.